Model predictive control is an optimization-based control strategy which has achieved enormous successes in numerous real-world applications. MPC generates control signals by means of real-time optimization of a performance index over a finite moving horizon of predicted future, subject to system constraints. A major challenge of the MPC research and development lies in the realization of nonlinear and robust MPC approaches, especially to cases where unmodeled dynamics exist. This chapter presents novel MPC approaches to nonlinear systems with unmodeled dynamics based on neural networks. At first, a nonlinear system with unmodeled dynamics is decomposed by means of Jacobian linearization to an affine part and a higher-order unknown term. The linearization residues, together with the unmodeled dynamics, are then modeled by using a feedforward neural network called the Extreme Learning Machine via supervised learning. When the controlled system is affected by bounded additive disturbances, the minimax methodology is exploited to achieve robustness. The nonlinear and robust MPC problems are formulated as constrained convex optimization problems and iteratively solved by applying neurodynamic optimization approaches. The applied neurodynamic optimization approaches can compute the optimal control signals in real-time, which shed a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approaches.
|Название основной публикации||Frontiers of Intelligent Control and Information Processing|
|Издатель||World Scientific Publishing Co.|
|ISBN (электронное издание)||9789814616881|
|ISBN (печатное издание)||9789814616874|
|Состояние||Опубликовано - 13 авг. 2014|
|Опубликовано для внешнего пользования||Да|